Overview

Dataset statistics

Number of variables15
Number of observations277938
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory31.8 MiB
Average record size in memory120.0 B

Variable types

Numeric13
Categorical1
Text1

Alerts

Unnamed: 0 is highly overall correlated with Unnamed: 0.1High correlation
Unnamed: 0.1 is highly overall correlated with Unnamed: 0High correlation
acousticness is highly overall correlated with energy and 2 other fieldsHigh correlation
danceability is highly overall correlated with valenceHigh correlation
duration (ms) is highly overall correlated with spec_rateHigh correlation
energy is highly overall correlated with acousticness and 2 other fieldsHigh correlation
labels is highly overall correlated with acousticness and 1 other fieldsHigh correlation
loudness is highly overall correlated with acousticness and 1 other fieldsHigh correlation
spec_rate is highly overall correlated with duration (ms) and 1 other fieldsHigh correlation
speechiness is highly overall correlated with spec_rateHigh correlation
valence is highly overall correlated with danceabilityHigh correlation
Unnamed: 0.1 is uniformly distributedUniform
Unnamed: 0 is uniformly distributedUniform
Unnamed: 0.1 has unique valuesUnique
Unnamed: 0 has unique valuesUnique
uri has unique valuesUnique
instrumentalness has 71748 (25.8%) zerosZeros

Reproduction

Analysis started2024-01-20 00:34:36.951539
Analysis finished2024-01-20 00:36:22.024244
Duration1 minute and 45.07 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

Unnamed: 0.1
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct277938
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean138968.5
Minimum0
Maximum277937
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-01-19T21:36:22.334934image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13896.85
Q169484.25
median138968.5
Q3208452.75
95-th percentile264040.15
Maximum277937
Range277937
Interquartile range (IQR)138968.5

Descriptive statistics

Standard deviation80233.934
Coefficient of variation (CV)0.57735339
Kurtosis-1.2
Mean138968.5
Median Absolute Deviation (MAD)69484.5
Skewness9.9864353 × 10-16
Sum3.8624627 × 1010
Variance6.4374841 × 109
MonotonicityStrictly increasing
2024-01-19T21:36:22.861551image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
185297 1
 
< 0.1%
185283 1
 
< 0.1%
185284 1
 
< 0.1%
185285 1
 
< 0.1%
185286 1
 
< 0.1%
185287 1
 
< 0.1%
185288 1
 
< 0.1%
185289 1
 
< 0.1%
185290 1
 
< 0.1%
Other values (277928) 277928
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
277937 1
< 0.1%
277936 1
< 0.1%
277935 1
< 0.1%
277934 1
< 0.1%
277933 1
< 0.1%
277932 1
< 0.1%
277931 1
< 0.1%
277930 1
< 0.1%
277929 1
< 0.1%
277928 1
< 0.1%

Unnamed: 0
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct277938
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean138968.5
Minimum0
Maximum277937
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-01-19T21:36:23.379559image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13896.85
Q169484.25
median138968.5
Q3208452.75
95-th percentile264040.15
Maximum277937
Range277937
Interquartile range (IQR)138968.5

Descriptive statistics

Standard deviation80233.934
Coefficient of variation (CV)0.57735339
Kurtosis-1.2
Mean138968.5
Median Absolute Deviation (MAD)69484.5
Skewness9.9864353 × 10-16
Sum3.8624627 × 1010
Variance6.4374841 × 109
MonotonicityStrictly increasing
2024-01-19T21:36:23.907769image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
185297 1
 
< 0.1%
185283 1
 
< 0.1%
185284 1
 
< 0.1%
185285 1
 
< 0.1%
185286 1
 
< 0.1%
185287 1
 
< 0.1%
185288 1
 
< 0.1%
185289 1
 
< 0.1%
185290 1
 
< 0.1%
Other values (277928) 277928
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
277937 1
< 0.1%
277936 1
< 0.1%
277935 1
< 0.1%
277934 1
< 0.1%
277933 1
< 0.1%
277932 1
< 0.1%
277931 1
< 0.1%
277930 1
< 0.1%
277929 1
< 0.1%
277928 1
< 0.1%

duration (ms)
Real number (ℝ)

HIGH CORRELATION 

Distinct114072
Distinct (%)41.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean232496.08
Minimum6706
Maximum3919895
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-01-19T21:36:24.727050image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum6706
5-th percentile112785.55
Q1172013
median213105.5
Q3264866
95-th percentile411494.9
Maximum3919895
Range3913189
Interquartile range (IQR)92853

Descriptive statistics

Standard deviation117183.02
Coefficient of variation (CV)0.50402148
Kurtosis97.197787
Mean232496.08
Median Absolute Deviation (MAD)45477
Skewness6.0626665
Sum6.4619496 × 1010
Variance1.373186 × 1010
MonotonicityNot monotonic
2024-01-19T21:36:25.321057image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
192000 264
 
0.1%
240000 227
 
0.1%
180000 221
 
0.1%
144000 162
 
0.1%
120000 161
 
0.1%
160000 158
 
0.1%
210000 150
 
0.1%
200000 149
 
0.1%
216000 147
 
0.1%
208000 146
 
0.1%
Other values (114062) 276153
99.4%
ValueCountFrequency (%)
6706 1
< 0.1%
7145 1
< 0.1%
7229 1
< 0.1%
7891 1
< 0.1%
8747 1
< 0.1%
8973 1
< 0.1%
9493 1
< 0.1%
9600 1
< 0.1%
11227 1
< 0.1%
11773 1
< 0.1%
ValueCountFrequency (%)
3919895 1
 
< 0.1%
3697969 1
 
< 0.1%
3653565 1
 
< 0.1%
3650064 1
 
< 0.1%
3634235 1
 
< 0.1%
3604214 1
 
< 0.1%
3603000 1
 
< 0.1%
3600000 4
< 0.1%
3473453 1
 
< 0.1%
3432107 1
 
< 0.1%

danceability
Real number (ℝ)

HIGH CORRELATION 

Distinct1327
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.55258339
Minimum0
Maximum0.989
Zeros90
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-01-19T21:36:25.917583image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.196
Q10.431
median0.571
Q30.693
95-th percentile0.833
Maximum0.989
Range0.989
Interquartile range (IQR)0.262

Descriptive statistics

Standard deviation0.18890482
Coefficient of variation (CV)0.34185758
Kurtosis-0.39338197
Mean0.55258339
Median Absolute Deviation (MAD)0.13
Skewness-0.38570711
Sum153583.92
Variance0.035685032
MonotonicityNot monotonic
2024-01-19T21:36:26.490426image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.565 636
 
0.2%
0.608 633
 
0.2%
0.633 624
 
0.2%
0.583 624
 
0.2%
0.594 618
 
0.2%
0.599 618
 
0.2%
0.588 617
 
0.2%
0.629 615
 
0.2%
0.552 612
 
0.2%
0.64 611
 
0.2%
Other values (1317) 271730
97.8%
ValueCountFrequency (%)
0 90
< 0.1%
0.0553 1
 
< 0.1%
0.0556 1
 
< 0.1%
0.0561 1
 
< 0.1%
0.0562 1
 
< 0.1%
0.0564 1
 
< 0.1%
0.0565 2
 
< 0.1%
0.0566 1
 
< 0.1%
0.0567 1
 
< 0.1%
0.0569 1
 
< 0.1%
ValueCountFrequency (%)
0.989 1
 
< 0.1%
0.988 3
 
< 0.1%
0.987 2
 
< 0.1%
0.986 2
 
< 0.1%
0.985 5
< 0.1%
0.984 2
 
< 0.1%
0.983 7
< 0.1%
0.982 1
 
< 0.1%
0.981 3
 
< 0.1%
0.98 8
< 0.1%

energy
Real number (ℝ)

HIGH CORRELATION 

Distinct2778
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.55686583
Minimum0
Maximum1
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-01-19T21:36:27.055477image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0533
Q10.342
median0.591
Q30.792
95-th percentile0.951
Maximum1
Range1
Interquartile range (IQR)0.45

Descriptive statistics

Standard deviation0.27968133
Coefficient of variation (CV)0.50224185
Kurtosis-0.97720293
Mean0.55686583
Median Absolute Deviation (MAD)0.219
Skewness-0.33499339
Sum154774.18
Variance0.078221644
MonotonicityNot monotonic
2024-01-19T21:36:27.630667image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.727 438
 
0.2%
0.932 407
 
0.1%
0.634 403
 
0.1%
0.931 403
 
0.1%
0.726 397
 
0.1%
0.73 397
 
0.1%
0.869 396
 
0.1%
0.912 396
 
0.1%
0.665 396
 
0.1%
0.871 396
 
0.1%
Other values (2768) 273909
98.6%
ValueCountFrequency (%)
0 4
< 0.1%
1.99 × 10-51
 
< 0.1%
2.01 × 10-51
 
< 0.1%
2.02 × 10-52
 
< 0.1%
2.03 × 10-55
< 0.1%
5.62 × 10-51
 
< 0.1%
7.15 × 10-51
 
< 0.1%
8.6 × 10-51
 
< 0.1%
0.000128 1
 
< 0.1%
0.000132 1
 
< 0.1%
ValueCountFrequency (%)
1 145
0.1%
0.999 192
0.1%
0.998 191
0.1%
0.997 210
0.1%
0.996 219
0.1%
0.995 276
0.1%
0.994 236
0.1%
0.993 255
0.1%
0.992 202
0.1%
0.991 257
0.1%

loudness
Real number (ℝ)

HIGH CORRELATION 

Distinct32201
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-10.363654
Minimum-60
Maximum4.882
Zeros0
Zeros (%)0.0%
Negative277624
Negative (%)99.9%
Memory size2.1 MiB
2024-01-19T21:36:28.180029image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-60
5-th percentile-24.885
Q1-12.747
median-8.397
Q3-5.842
95-th percentile-3.421
Maximum4.882
Range64.882
Interquartile range (IQR)6.905

Descriptive statistics

Standard deviation6.6720495
Coefficient of variation (CV)-0.64379315
Kurtosis2.8428217
Mean-10.363654
Median Absolute Deviation (MAD)3.073
Skewness-1.6053681
Sum-2880453.3
Variance44.516244
MonotonicityNot monotonic
2024-01-19T21:36:28.739422image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-6.64 48
 
< 0.1%
-6.005 47
 
< 0.1%
-6.903 47
 
< 0.1%
-6.008 46
 
< 0.1%
-6.472 46
 
< 0.1%
-7.143 45
 
< 0.1%
-6.526 44
 
< 0.1%
-6.099 44
 
< 0.1%
-7.136 44
 
< 0.1%
-6.162 44
 
< 0.1%
Other values (32191) 277483
99.8%
ValueCountFrequency (%)
-60 3
< 0.1%
-58.66 1
 
< 0.1%
-53.965 1
 
< 0.1%
-53.873 1
 
< 0.1%
-52.363 1
 
< 0.1%
-51.24 1
 
< 0.1%
-50.862 1
 
< 0.1%
-49.092 1
 
< 0.1%
-49.009 1
 
< 0.1%
-48.77 1
 
< 0.1%
ValueCountFrequency (%)
4.882 1
< 0.1%
4.815 1
< 0.1%
4.584 1
< 0.1%
4.142 1
< 0.1%
3.795 1
< 0.1%
3.642 1
< 0.1%
2.994 1
< 0.1%
2.824 1
< 0.1%
2.744 1
< 0.1%
2.704 1
< 0.1%

speechiness
Real number (ℝ)

HIGH CORRELATION 

Distinct1640
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.087913272
Minimum0
Maximum0.965
Zeros90
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-01-19T21:36:29.315098image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0285
Q10.0359
median0.0471
Q30.0822
95-th percentile0.306
Maximum0.965
Range0.965
Interquartile range (IQR)0.0463

Descriptive statistics

Standard deviation0.11250014
Coefficient of variation (CV)1.2796718
Kurtosis22.055991
Mean0.087913272
Median Absolute Deviation (MAD)0.0146
Skewness4.0972955
Sum24434.439
Variance0.01265628
MonotonicityNot monotonic
2024-01-19T21:36:29.870632image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0337 854
 
0.3%
0.0362 841
 
0.3%
0.0345 841
 
0.3%
0.0336 829
 
0.3%
0.0323 829
 
0.3%
0.0324 828
 
0.3%
0.0334 826
 
0.3%
0.035 824
 
0.3%
0.0343 824
 
0.3%
0.0356 821
 
0.3%
Other values (1630) 269621
97.0%
ValueCountFrequency (%)
0 90
< 0.1%
0.022 1
 
< 0.1%
0.0221 2
 
< 0.1%
0.0222 2
 
< 0.1%
0.0223 6
 
< 0.1%
0.0224 5
 
< 0.1%
0.0225 3
 
< 0.1%
0.0226 6
 
< 0.1%
0.0227 10
 
< 0.1%
0.0228 9
 
< 0.1%
ValueCountFrequency (%)
0.965 3
 
< 0.1%
0.964 1
 
< 0.1%
0.963 1
 
< 0.1%
0.962 2
 
< 0.1%
0.961 8
 
< 0.1%
0.96 7
 
< 0.1%
0.959 15
< 0.1%
0.958 14
< 0.1%
0.957 10
< 0.1%
0.956 20
< 0.1%

acousticness
Real number (ℝ)

HIGH CORRELATION 

Distinct5177
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.38658274
Minimum0
Maximum0.996
Zeros51
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-01-19T21:36:30.422653image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.00044985
Q10.0338
median0.262
Q30.754
95-th percentile0.985
Maximum0.996
Range0.996
Interquartile range (IQR)0.7202

Descriptive statistics

Standard deviation0.36450433
Coefficient of variation (CV)0.94288827
Kurtosis-1.3884633
Mean0.38658274
Median Absolute Deviation (MAD)0.25639
Skewness0.44518238
Sum107446.03
Variance0.13286341
MonotonicityNot monotonic
2024-01-19T21:36:30.994971image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.995 2163
 
0.8%
0.994 1883
 
0.7%
0.993 1671
 
0.6%
0.992 1373
 
0.5%
0.991 1173
 
0.4%
0.99 991
 
0.4%
0.989 980
 
0.4%
0.988 931
 
0.3%
0.986 782
 
0.3%
0.987 778
 
0.3%
Other values (5167) 265213
95.4%
ValueCountFrequency (%)
0 51
< 0.1%
1 × 10-62
 
< 0.1%
1.01 × 10-64
 
< 0.1%
1.02 × 10-62
 
< 0.1%
1.03 × 10-61
 
< 0.1%
1.05 × 10-63
 
< 0.1%
1.07 × 10-61
 
< 0.1%
1.08 × 10-61
 
< 0.1%
1.09 × 10-61
 
< 0.1%
1.1 × 10-63
 
< 0.1%
ValueCountFrequency (%)
0.996 730
 
0.3%
0.995 2163
0.8%
0.994 1883
0.7%
0.993 1671
0.6%
0.992 1373
0.5%
0.991 1173
0.4%
0.99 991
0.4%
0.989 980
0.4%
0.988 931
0.3%
0.987 778
 
0.3%

instrumentalness
Real number (ℝ)

ZEROS 

Distinct5402
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.25504435
Minimum0
Maximum1
Zeros71748
Zeros (%)25.8%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-01-19T21:36:31.553473image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.00109
Q30.645
95-th percentile0.934
Maximum1
Range1
Interquartile range (IQR)0.645

Descriptive statistics

Standard deviation0.37374458
Coefficient of variation (CV)1.4654102
Kurtosis-0.88732226
Mean0.25504435
Median Absolute Deviation (MAD)0.00109
Skewness0.97192936
Sum70886.516
Variance0.13968501
MonotonicityNot monotonic
2024-01-19T21:36:32.123746image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 71748
 
25.8%
0.899 493
 
0.2%
0.927 489
 
0.2%
0.925 481
 
0.2%
0.918 478
 
0.2%
0.922 473
 
0.2%
0.929 467
 
0.2%
0.932 465
 
0.2%
0.924 464
 
0.2%
0.923 463
 
0.2%
Other values (5392) 201917
72.6%
ValueCountFrequency (%)
0 71748
25.8%
1 × 10-645
 
< 0.1%
1.01 × 10-6100
 
< 0.1%
1.02 × 10-698
 
< 0.1%
1.03 × 10-684
 
< 0.1%
1.04 × 10-6101
 
< 0.1%
1.05 × 10-688
 
< 0.1%
1.06 × 10-685
 
< 0.1%
1.07 × 10-697
 
< 0.1%
1.08 × 10-689
 
< 0.1%
ValueCountFrequency (%)
1 2
 
< 0.1%
0.999 11
 
< 0.1%
0.998 13
 
< 0.1%
0.997 21
 
< 0.1%
0.996 18
 
< 0.1%
0.995 20
 
< 0.1%
0.994 37
< 0.1%
0.993 29
< 0.1%
0.992 27
< 0.1%
0.991 55
< 0.1%

liveness
Real number (ℝ)

Distinct1766
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.18921667
Minimum0
Maximum1
Zeros9
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-01-19T21:36:32.690595image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0622
Q10.0962
median0.121
Q30.227
95-th percentile0.548
Maximum1
Range1
Interquartile range (IQR)0.1308

Descriptive statistics

Standard deviation0.16359636
Coefficient of variation (CV)0.86459799
Kurtosis6.3675305
Mean0.18921667
Median Absolute Deviation (MAD)0.0382
Skewness2.3952294
Sum52590.504
Variance0.026763768
MonotonicityNot monotonic
2024-01-19T21:36:33.259084image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.111 4198
 
1.5%
0.11 3943
 
1.4%
0.109 3690
 
1.3%
0.108 3512
 
1.3%
0.112 3449
 
1.2%
0.107 3349
 
1.2%
0.106 3155
 
1.1%
0.105 3133
 
1.1%
0.104 3030
 
1.1%
0.103 2928
 
1.1%
Other values (1756) 243551
87.6%
ValueCountFrequency (%)
0 9
< 0.1%
0.00785 1
 
< 0.1%
0.00918 1
 
< 0.1%
0.00967 1
 
< 0.1%
0.01 2
 
< 0.1%
0.0101 1
 
< 0.1%
0.0102 1
 
< 0.1%
0.0109 1
 
< 0.1%
0.0114 1
 
< 0.1%
0.012 1
 
< 0.1%
ValueCountFrequency (%)
1 1
 
< 0.1%
0.998 1
 
< 0.1%
0.997 1
 
< 0.1%
0.996 2
 
< 0.1%
0.995 6
 
< 0.1%
0.994 7
< 0.1%
0.993 8
< 0.1%
0.992 11
< 0.1%
0.991 6
 
< 0.1%
0.99 17
< 0.1%

valence
Real number (ℝ)

HIGH CORRELATION 

Distinct1941
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.44960181
Minimum0
Maximum1
Zeros135
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-01-19T21:36:33.813811image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0434
Q10.22
median0.434
Q30.665
95-th percentile0.904
Maximum1
Range1
Interquartile range (IQR)0.445

Descriptive statistics

Standard deviation0.26747136
Coefficient of variation (CV)0.59490721
Kurtosis-1.0637139
Mean0.44960181
Median Absolute Deviation (MAD)0.222
Skewness0.17384783
Sum124961.43
Variance0.071540929
MonotonicityNot monotonic
2024-01-19T21:36:34.389724image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.961 645
 
0.2%
0.962 560
 
0.2%
0.963 494
 
0.2%
0.964 450
 
0.2%
0.96 404
 
0.1%
0.965 401
 
0.1%
0.196 399
 
0.1%
0.356 394
 
0.1%
0.392 394
 
0.1%
0.357 388
 
0.1%
Other values (1931) 273409
98.4%
ValueCountFrequency (%)
0 135
< 0.1%
1 × 10-5311
0.1%
2.08 × 10-51
 
< 0.1%
3.64 × 10-51
 
< 0.1%
0.000119 1
 
< 0.1%
0.000331 1
 
< 0.1%
0.000436 1
 
< 0.1%
0.000453 1
 
< 0.1%
0.000621 1
 
< 0.1%
0.000622 1
 
< 0.1%
ValueCountFrequency (%)
1 4
 
< 0.1%
0.999 1
 
< 0.1%
0.998 1
 
< 0.1%
0.996 1
 
< 0.1%
0.995 2
 
< 0.1%
0.994 3
 
< 0.1%
0.993 3
 
< 0.1%
0.992 3
 
< 0.1%
0.991 3
 
< 0.1%
0.99 10
< 0.1%

tempo
Real number (ℝ)

Distinct93680
Distinct (%)33.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean119.196
Minimum0
Maximum244.947
Zeros90
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-01-19T21:36:34.933743image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile74.73155
Q195.07225
median119.94
Q3138.86975
95-th percentile174.14515
Maximum244.947
Range244.947
Interquartile range (IQR)43.7975

Descriptive statistics

Standard deviation30.462256
Coefficient of variation (CV)0.25556441
Kurtosis-0.30242625
Mean119.196
Median Absolute Deviation (MAD)21.66
Skewness0.31821845
Sum33129099
Variance927.94907
MonotonicityNot monotonic
2024-01-19T21:36:35.487507image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120.008 100
 
< 0.1%
120.004 93
 
< 0.1%
0 90
 
< 0.1%
120.001 89
 
< 0.1%
120.007 86
 
< 0.1%
120.003 84
 
< 0.1%
120.013 84
 
< 0.1%
120.005 83
 
< 0.1%
120.009 81
 
< 0.1%
119.998 80
 
< 0.1%
Other values (93670) 277068
99.7%
ValueCountFrequency (%)
0 90
< 0.1%
30.186 1
 
< 0.1%
30.762 1
 
< 0.1%
31.084 1
 
< 0.1%
31.27 1
 
< 0.1%
31.47 1
 
< 0.1%
31.587 1
 
< 0.1%
31.967 2
 
< 0.1%
31.988 1
 
< 0.1%
32.074 1
 
< 0.1%
ValueCountFrequency (%)
244.947 1
< 0.1%
241.808 1
< 0.1%
241.423 1
< 0.1%
240.118 1
< 0.1%
239.942 1
< 0.1%
236.059 1
< 0.1%
233.933 1
< 0.1%
232.5 1
< 0.1%
232.124 1
< 0.1%
232.031 1
< 0.1%

spec_rate
Real number (ℝ)

HIGH CORRELATION 

Distinct271727
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7546539 × 10-7
Minimum0
Maximum5.9718597 × 10-5
Zeros90
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-01-19T21:36:36.017728image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.560064 × 10-8
Q11.5314613 × 10-7
median2.3454591 × 10-7
Q34.449937 × 10-7
95-th percentile1.6326421 × 10-6
Maximum5.9718597 × 10-5
Range5.9718597 × 10-5
Interquartile range (IQR)2.9184757 × 10-7

Descriptive statistics

Standard deviation9.1902295 × 10-7
Coefficient of variation (CV)1.9328914
Kurtosis555.71092
Mean4.7546539 × 10-7
Median Absolute Deviation (MAD)1.0355598 × 10-7
Skewness16.050351
Sum0.1321499
Variance8.4460318 × 10-13
MonotonicityNot monotonic
2024-01-19T21:36:36.576235image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 90
 
< 0.1%
2.5 × 10-720
 
< 0.1%
1.5 × 10-717
 
< 0.1%
3 × 10-714
 
< 0.1%
1.5625 × 10-713
 
< 0.1%
5 × 10-712
 
< 0.1%
1.875 × 10-712
 
< 0.1%
2 × 10-712
 
< 0.1%
1.666666667 × 10-711
 
< 0.1%
2.666666667 × 10-79
 
< 0.1%
Other values (271717) 277728
99.9%
ValueCountFrequency (%)
0 90
< 0.1%
9.464539229 × 10-91
 
< 0.1%
1.017721648 × 10-81
 
< 0.1%
1.021081294 × 10-81
 
< 0.1%
1.065717172 × 10-81
 
< 0.1%
1.084134625 × 10-81
 
< 0.1%
1.196711281 × 10-81
 
< 0.1%
1.23988488 × 10-81
 
< 0.1%
1.249770707 × 10-81
 
< 0.1%
1.306826401 × 10-81
 
< 0.1%
ValueCountFrequency (%)
5.97185968 × 10-51
< 0.1%
5.555884267 × 10-51
< 0.1%
5.342960289 × 10-51
< 0.1%
5.270758123 × 10-51
< 0.1%
5.138339921 × 10-51
< 0.1%
4.948486628 × 10-51
< 0.1%
4.941090985 × 10-51
< 0.1%
4.9367221 × 10-51
< 0.1%
4.099893162 × 10-51
< 0.1%
4.040925517 × 10-51
< 0.1%

labels
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
1
106429 
0
82058 
2
47065 
3
42386 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters277938
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 106429
38.3%
0 82058
29.5%
2 47065
16.9%
3 42386
 
15.3%

Length

2024-01-19T21:36:37.084779image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-19T21:36:37.488307image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
1 106429
38.3%
0 82058
29.5%
2 47065
16.9%
3 42386
 
15.3%

Most occurring characters

ValueCountFrequency (%)
1 106429
38.3%
0 82058
29.5%
2 47065
16.9%
3 42386
 
15.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 277938
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 106429
38.3%
0 82058
29.5%
2 47065
16.9%
3 42386
 
15.3%

Most occurring scripts

ValueCountFrequency (%)
Common 277938
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 106429
38.3%
0 82058
29.5%
2 47065
16.9%
3 42386
 
15.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 277938
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 106429
38.3%
0 82058
29.5%
2 47065
16.9%
3 42386
 
15.3%

uri
Text

UNIQUE 

Distinct277938
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
2024-01-19T21:36:38.581520image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters10005768
Distinct characters63
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique277938 ?
Unique (%)100.0%

Sample

1st rowspotify:track:3v6sBj3swihU8pXQQHhDZo
2nd rowspotify:track:7KCWmFdw0TzoJbKtqRRzJO
3rd rowspotify:track:2CY92qejUrhyPUASawNVRr
4th rowspotify:track:11BPfwVbB7vok7KfjBeW4k
5th rowspotify:track:3yUJKPsjvThlcQWTS9ttYx
ValueCountFrequency (%)
spotify:track:3v6sbj3swihu8pxqqhhdzo 1
 
< 0.1%
spotify:track:03ibpduyi7g1voc9ejgrsy 1
 
< 0.1%
spotify:track:1i6guygzjf7xmukcwswj7k 1
 
< 0.1%
spotify:track:5x4b5yuoihfpbzmzsohg8m 1
 
< 0.1%
spotify:track:4ksq9el0qgtkkie5octkle 1
 
< 0.1%
spotify:track:2cy92qejurhypuasawnvrr 1
 
< 0.1%
spotify:track:11bpfwvbb7vok7kfjbew4k 1
 
< 0.1%
spotify:track:3yujkpsjvthlcqwts9ttyx 1
 
< 0.1%
spotify:track:41mocunogwtaybfusgnpz5 1
 
< 0.1%
spotify:track:5jp1cmcdxx4k2gwfsgt8lf 1
 
< 0.1%
Other values (277928) 277928
> 99.9%
2024-01-19T21:36:39.942195image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 650439
 
6.5%
: 555876
 
5.6%
y 372979
 
3.7%
k 372517
 
3.7%
o 372430
 
3.7%
i 372386
 
3.7%
r 372374
 
3.7%
c 372230
 
3.7%
f 372134
 
3.7%
s 372129
 
3.7%
Other values (53) 5820274
58.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5786455
57.8%
Uppercase Letter 2443312
24.4%
Decimal Number 1220125
 
12.2%
Other Punctuation 555876
 
5.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 650439
 
11.2%
y 372979
 
6.4%
k 372517
 
6.4%
o 372430
 
6.4%
i 372386
 
6.4%
r 372374
 
6.4%
c 372230
 
6.4%
f 372134
 
6.4%
s 372129
 
6.4%
p 372051
 
6.4%
Other values (16) 1784786
30.8%
Uppercase Letter
ValueCountFrequency (%)
K 94644
 
3.9%
A 94548
 
3.9%
D 94521
 
3.9%
H 94308
 
3.9%
Z 94297
 
3.9%
E 94296
 
3.9%
C 94288
 
3.9%
F 94251
 
3.9%
J 94235
 
3.9%
G 94233
 
3.9%
Other values (16) 1499691
61.4%
Decimal Number
ValueCountFrequency (%)
1 130625
10.7%
3 129811
10.6%
0 129804
10.6%
6 129803
10.6%
5 129619
10.6%
2 129583
10.6%
4 129276
10.6%
7 122767
10.1%
9 94487
7.7%
8 94350
7.7%
Other Punctuation
ValueCountFrequency (%)
: 555876
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8229767
82.3%
Common 1776001
 
17.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 650439
 
7.9%
y 372979
 
4.5%
k 372517
 
4.5%
o 372430
 
4.5%
i 372386
 
4.5%
r 372374
 
4.5%
c 372230
 
4.5%
f 372134
 
4.5%
s 372129
 
4.5%
p 372051
 
4.5%
Other values (42) 4228098
51.4%
Common
ValueCountFrequency (%)
: 555876
31.3%
1 130625
 
7.4%
3 129811
 
7.3%
0 129804
 
7.3%
6 129803
 
7.3%
5 129619
 
7.3%
2 129583
 
7.3%
4 129276
 
7.3%
7 122767
 
6.9%
9 94487
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10005768
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 650439
 
6.5%
: 555876
 
5.6%
y 372979
 
3.7%
k 372517
 
3.7%
o 372430
 
3.7%
i 372386
 
3.7%
r 372374
 
3.7%
c 372230
 
3.7%
f 372134
 
3.7%
s 372129
 
3.7%
Other values (53) 5820274
58.2%

Interactions

2024-01-19T21:36:13.308960image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:06.888974image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:12.520946image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:18.267478image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:24.010849image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:29.452304image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:34.939302image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:40.657994image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:45.985239image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:51.410551image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:56.792743image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:36:02.663803image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:36:07.994028image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:36:13.709959image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:07.374054image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:12.941328image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:18.709268image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:24.420443image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:29.888223image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:35.355597image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:41.066174image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:46.397357image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:51.817606image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:57.199910image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:36:03.210673image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:36:08.387785image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:36:14.118644image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:07.811130image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:13.436269image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:19.157810image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:24.833283image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:30.313455image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:35.771367image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:41.466077image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:46.810518image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:52.235017image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:57.613266image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:36:03.596614image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:36:08.777804image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:36:14.565538image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:08.269828image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:13.938484image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:19.626508image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:25.286326image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:30.850688image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:36.235487image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:41.919710image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:47.279957image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:52.691194image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:58.073170image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:36:04.036336image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:36:09.232417image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:36:14.970281image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:08.664129image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:14.356539image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:20.049020image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:25.700912image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:31.251735image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:36.650290image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:42.318691image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:47.690661image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:53.104551image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:58.478513image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:36:04.483286image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:36:09.630902image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:36:15.367943image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:09.061155image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:14.825346image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:20.499712image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:26.115758image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:31.663080image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:37.067460image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:42.721548image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:48.103485image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:53.506211image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:58.885268image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:36:04.898320image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:36:10.033618image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:36:15.785567image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:09.464387image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:15.212331image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:20.964623image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:26.543700image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:32.087539image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:37.492486image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:43.134856image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:48.533192image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:53.937824image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:59.317192image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:36:05.268923image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:36:10.442566image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:36:16.190071image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:09.865569image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:15.591238image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:21.405770image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:26.954684image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:32.496327image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:37.910495image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:43.539322image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:48.934982image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:54.339541image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:59.733088image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:36:05.654713image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:36:10.864908image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:36:16.596151image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:10.262594image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:16.185549image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:21.832613image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:27.364044image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:32.900216image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:38.588154image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:43.940152image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:49.343231image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:54.747919image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:36:00.173538image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:36:06.031126image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:36:11.273799image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:36:16.997649image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:10.660787image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:16.599169image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:22.258614image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:27.786720image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:33.309296image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:39.002234image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:44.344627image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:49.756725image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:55.148223image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:36:00.636426image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:36:06.423346image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:36:11.671735image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:36:17.404756image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:11.082315image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:17.011813image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:22.683536image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:28.192408image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:33.706108image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:39.413527image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:44.744657image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:50.172663image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:55.551064image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:36:01.058559image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:36:06.805588image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:36:12.075661image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:36:17.805631image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:11.607810image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:17.437571image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:23.129500image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:28.599400image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:34.107707image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:39.827127image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:45.144816image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:50.579808image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:55.958925image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:36:01.488055image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:36:07.197108image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:36:12.471768image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:36:18.200764image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:12.058604image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:17.843636image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:23.556760image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:29.002656image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:34.515089image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:40.234051image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:45.557069image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:50.990225image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:35:56.359988image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:36:02.210097image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:36:07.585837image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-19T21:36:12.900193image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-01-19T21:36:40.386791image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Unnamed: 0Unnamed: 0.1acousticnessdanceabilityduration (ms)energyinstrumentalnesslabelslivenessloudnessspec_ratespeechinesstempovalence
Unnamed: 01.0001.0000.0400.061-0.056-0.060-0.0340.156-0.034-0.0040.012-0.024-0.029-0.016
Unnamed: 0.11.0001.0000.0400.061-0.056-0.060-0.0340.156-0.034-0.0040.012-0.024-0.029-0.016
acousticness0.0400.0401.000-0.236-0.157-0.7610.2190.528-0.122-0.649-0.080-0.200-0.254-0.222
danceability0.0610.061-0.2361.000-0.0780.232-0.2450.365-0.0720.2760.2410.2490.0230.506
duration (ms)-0.056-0.056-0.157-0.0781.0000.1100.0330.056-0.0320.060-0.587-0.1140.038-0.097
energy-0.060-0.060-0.7610.2320.1101.000-0.2780.5990.2100.8130.1750.2870.2680.370
instrumentalness-0.034-0.0340.219-0.2450.033-0.2781.0000.454-0.131-0.478-0.173-0.153-0.085-0.347
labels0.1560.1560.5280.3650.0560.5990.4541.000-0.0240.0020.2610.2380.034-0.119
liveness-0.034-0.034-0.122-0.072-0.0320.210-0.131-0.0241.0000.1590.1000.0930.0410.042
loudness-0.004-0.004-0.6490.2760.0600.813-0.4780.0020.1591.0000.1490.2020.2300.351
spec_rate0.0120.012-0.0800.241-0.5870.175-0.1730.2610.1000.1491.0000.8270.0530.166
speechiness-0.024-0.024-0.2000.249-0.1140.287-0.1530.2380.0930.2020.8271.0000.1010.140
tempo-0.029-0.029-0.2540.0230.0380.268-0.0850.0340.0410.2300.0530.1011.0000.136
valence-0.016-0.016-0.2220.506-0.0970.370-0.347-0.1190.0420.3510.1660.1400.1361.000

Missing values

2024-01-19T21:36:18.789712image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-19T21:36:20.147174image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0.1Unnamed: 0duration (ms)danceabilityenergyloudnessspeechinessacousticnessinstrumentalnesslivenessvalencetempospec_ratelabelsuri
000195000.00.6110.614-8.8150.06720.01690.0007940.75300.520128.0503.446154e-072spotify:track:3v6sBj3swihU8pXQQHhDZo
111194641.00.6380.781-6.8480.02850.01180.0095300.34900.250122.9851.464234e-071spotify:track:7KCWmFdw0TzoJbKtqRRzJO
222217573.00.5600.810-8.0290.08720.00710.0000080.24100.247170.0444.007850e-071spotify:track:2CY92qejUrhyPUASawNVRr
333443478.00.5250.699-4.5710.03530.01780.0000880.08880.19992.0117.959809e-080spotify:track:11BPfwVbB7vok7KfjBeW4k
444225862.00.3670.771-5.8630.10600.36500.0000010.09650.163115.9174.693131e-071spotify:track:3yUJKPsjvThlcQWTS9ttYx
555166920.00.5720.837-7.8760.03670.01970.0000000.16300.627100.3432.198658e-071spotify:track:41MOCUNOgWtaYBFUsGnpZ5
666193133.00.7250.687-6.4650.05960.69400.0003690.23100.77096.0053.085956e-071spotify:track:5JP1cMCDxX4k2gwfSgt8Lf
777253000.00.6750.547-4.9990.04810.11400.0000800.06780.36575.0031.901186e-071spotify:track:73xsMXuRNB3yqLeNc7NXBq
888216187.00.5160.692-4.8420.02790.08750.0093000.09000.18183.5711.290549e-070spotify:track:6TwrBbgTaB5gpl06YQoRKy
999232333.00.5480.509-7.9370.02880.26100.7020000.07900.48478.9741.239600e-070spotify:track:5SDEirHg6Y8fCYuKMnAaC5
Unnamed: 0.1Unnamed: 0duration (ms)danceabilityenergyloudnessspeechinessacousticnessinstrumentalnesslivenessvalencetempospec_ratelabelsuri
277928277928277928369600.00.4820.721-6.8390.03210.731000.0000000.18900.55795.2638.685065e-080spotify:track:3AhXZa8sUQht0UEdBJgpGc
277929277929277929295893.00.6710.373-18.0640.03230.257000.0000800.04810.73292.7181.091611e-071spotify:track:2374M0fQpWi3dLnB54qaLX
277930277930277930187600.00.7490.484-9.9440.04840.130000.1370000.04860.939167.0842.579957e-071spotify:track:7HX9f8AMBl0vQxAoAzLqhS
277931277931277931236520.00.6630.712-7.2680.04790.524000.0000000.08040.527113.4392.025199e-071spotify:track:2aSFLiDPreOVP6KHiWk4lF
277932277932277932220867.00.7460.790-4.7000.02570.048500.0261000.05340.891107.2311.163596e-071spotify:track:46RVKt5Edm1zl0rXhPJZxz
277933277933277933276360.00.7770.725-9.0120.04700.126000.0108000.09170.851128.3491.700680e-071spotify:track:6wLr2oR8eqUG5Beleh2Crm
277934277934277934284773.00.5430.482-12.7890.19400.085300.0000920.11100.415193.5136.812444e-071spotify:track:5mYtpXrZZ1bbGJYDGC8I0Y
277935277935277935241307.00.5270.942-5.6400.03660.011500.0000000.18800.495148.7231.516740e-072spotify:track:7FwBtcecmlpc1sLySPXeGE
277936277936277936234333.00.7680.829-5.1090.03130.096400.0000290.09700.962118.7731.335706e-071spotify:track:2olVm1lHicpveMAo4AUDRB
277937277937277937241920.00.7790.870-13.1410.05740.006440.0107000.03990.555102.6892.372685e-071spotify:track:2VNfJpwdEQBLyXajaa6LWT